Bike-sharing Usage Research Articles

A Study on Bike Share Analysis

This study explores the role of bike-sharing programs within urban mobility and the potential impacts of autonomous vehicles (AVs) on cyclists' safety perceptions and behaviour. The research focuses on analysing factors that influence the adoption of bike-sharing services, including safety perceptions, AV technology familiarity, and demographic characteristics. Using tools like MS Excel and statistical models such as multiple regression and logistic regression, the study examines patterns in bike-sharing usage, public attitudes toward AVs, and the implications of AV testing on public streets. Findings suggest that factors like age, perceived safety, and AV familiarity significantly influence bike-sharing behaviour. The study recommends enhanced infrastructure, integration with public transit, and data-driven insights to improve bike-sharing systems and promote safer interactions with AVs

Success in tandem? The impact of the introduction of e-bike sharing on bike sharing usage

In recent years, shared electric bikes (e-bikes), as a novel shared micro-mobility mode, have experienced a significant increase in popularity. Yet, there is still limited evidence on whether the two forms of micro-mobility act as complements or substitutes. By exploring the effects of the entry of shared e-bikes on bike-sharing and overall shared cycling usage, this paper aims to explore the role of e-bikes in sustainable transportation solutions. The paper combines nearest-neighbour matching and a difference-in-differences event study with bi-monthly panel data of conventional bike-sharing and e-bike-sharing trips from 2020 to 2021. Results suggest that the effect of e-bike-share on bike-sharing at the grid level shifted from an initial complementary effect to a later substitution effect, ultimately resulting in a 22.6% reduction in bike-sharing usage and a 65.9% increase in overall shared cycling usage. The introduction of shared e-bikes simultaneously attracted new user groups and reduced existing barriers to using conventional bikes. With growing market saturation, any further expansion is found to have come to the detriment of the incumbent technology, i.e. the conventional pedal bike. Although shared e-bikes ultimately replaced part of shared bikes, they have increased the overall use of micro-mobility tools, contributing to the popularization of sustainable transportation.

Understanding the determinants of bike-sharing demand in the context of a medium-sized car-oriented city: The case study of Milton Keynes, UK

Emerging modes of shared mobility like bike-sharing may significantly contribute to increasing the share of sustainable travel in medium- and small-sized cities. However, research exploring the bike-sharing usage determinants specifically for them has been severely underrepresented in the literature. To address this gap, we conducted an analysis of bike-sharing rentals for over a one-year period in Milton Keynes, a medium-sized city of 288,000 residents. Being the largest of the planned settlements built under the UK government “New Towns” programme, Milton Keynes despite having the infrastructure to offer opportunities for modal shift has been repeatedly characterised as a car-centric city. The paper examines temporal variation by comparing the average number of bike rentals in different seasons and months. Analysis of peak rentals and travel directions suggests that throughout the year bikes are mainly used to travel to work. By contrast, recreational travel appears to happen mostly during the summer months. Regression analysis shows that the number of public transport stops, offices and schools are associated with a higher number of bike rentals, supporting the view that utilitarian travel is the main usage pattern. Our research also highlights the bike-sharing's potential to be adopted as a first/last-mile option that will complement and support public transit.

Forecasting the Usage of Bike-Sharing Systems through Machine Learning Techniques to Foster Sustainable Urban Mobility

Bike-sharing systems can definitely contribute to the achievement of sustainable urban mobility. In spite of this potential, their planning and operation are not free of difficulties. The main operational problem of bike-sharing systems is the unbalanced distribution of bicycles over the service region, resulting in zones where bicycles are scarce and zones where bicycles accumulate. In order to provide an acceptable level of service, the operator needs to carry out repositioning movements, which are costly. Bike-sharing repositioning optimization solutions have been developed that rely on the estimation of the expected number of requests and returns at each location. Errors in this prediction are directly transferred to suboptimal repositioning solutions. For this reason, the development of methodologies able to accurately forecast bike-sharing usage is an issue of great concern. This paper deals with this problem using machine learning regression methods, which yield usage predictions from inputs such as historical usage and meteorological data. Three different machine learning regression techniques have been analyzed (i.e., random forest, gradient boosting, and artificial neural networks) and applied to a case study based on the New York City bike-sharing system. This paper describes the variables of the models and their calibration processes. Results are analyzed and compared in order to determine which one of the three techniques and under what conditions is the most adequate. Comparisons are not only made in terms of accuracy but also with respect to the applicability of the algorithms. Results indicate that, given the similar accuracy of all methods, the simpler calibration process of the random forest technique makes it advisable for most applications.

Research on the Urban Bike-sharing Usage based on ARIMA Model

Research on the Urban Bike-sharing Usage based on ARIMA Model

Developing deep learning models for predicting urban bike-sharing usage patterns

Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.

The impact of the social-built environment on the inequity of bike-sharing use: A case study of Divvy system in Chicago

Bikeshare is increasingly recognized as a healthy travel behaviour worldwide. However, issues of inequity in bike-sharing usage exist and hinder the social benefits of bike-sharing system. This paper aims to unveil the spatiotemporal evolution of inequalities in bike-sharing usage and their social-built environment correlates, using Chicago’s Divvy system as a case study. Specifically, Gini coefficients and panel data regression models are applied to analyse equity concerns in bike-sharing uses and its social-built environmental factors. Thirty-two disadvantaged communities and forty-five non-disadvantaged communities are identified based on ethnicity, income, and education levels. The Gini index indicates a greater level of inequity and inconsistency in bike-sharing usage within disadvantaged communities compared to non-disadvantaged communities over time. Model results further reveal that built environment factors such as park space positively impact equitable bike-sharing uses in disadvantaged communities. In contrast, the social factor of educational levels in non-disadvantaged communities shows a negative relationship. These findings aim to promote essential, efficient, and equitable bike-sharing usage for Chicago, stakeholders and users.

Unlocking Trends: Socio-Demographic Insights into Bike Sharing from the 2017 National Household Travel Survey

The implementation of bike-sharing systems represents a novel strategy aimed at enhancing urban mobility by offering shared bicycle services to improve accessibility and user convenience. Nevertheless, there has been a limited exploration into the user demographics of bike-sharing programs on a national scale. This study investigates the socio-demographic characteristics of bike-sharing system users, leveraging the 2017 National Household Travel Survey and negative binomial regression models. It finds that bike-sharing usage is more common among users than non-users, with African American and Hispanic individuals using the system more than other racial groups. Furthermore, low-income individuals (<USD 15,000) and residents of high-density areas (≥25,000 per square mile) show a higher tendency towards bike sharing. The research highlights the lack of national-level studies on bike-sharing user demographics and suggests that future policy and planning should address the needs of specific populations, aiming to enhance urban mobility and accessibility through bike-sharing services.

Impact of COVID-19 pandemic on characteristic of bike-sharing systems near metro and bus stations

ABSTRACT The COVID-19 pandemic has had a significant impact on various modes of mobility as it spread rapidly worldwide, leading to changes in the landscape of transportation. This research focuses on comparing the usage of bike-sharing and the shifts in cycling behaviour before (2019) and after (2020) the pandemic outbreak in Daejeon, South Korea. Additionally, it examines the role of bike-sharing as a supplementary mode of transport for public transportation systems such as buses and metros during these periods. Our analysis revealed that bike-sharing has become a more prominent mode of transport after the pandemic. We developed a model to calculate the demand ratio of bike-sharing within the catchment areas of bus and metro stations to assess its impact as a supplementary mode. The results indicate an increase in the usage of some dockless bike stations in the catchment areas of buses and metros by 0.22% and 7.29%, respectively. We also conducted a correlation analysis using Point of Interest (POI) data to understand the factors influencing bike-sharing usage. The findings suggest that travel behaviours have shifted towards commercial and recreational destinations. Given its cost-effectiveness and flexibility, bike-sharing could be a sustainable option for urban mobility. Therefore, city planners in Daejeon can use the results of this study to promote bike-sharing and cycling as viable transportation alternatives.

Benefits, barriers and willingness to pay for bike-sharing service in tourism context

This study explores benefits, barriers and willingness to pay for bike-sharing service in tourism context. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, trends in the barriers and benefits related to bike-sharing service from tourists’ point-of-view were explored. The results show that bike-sharing is appreciated for many reasons, notably for its fun/relaxing, cost saving, ease of city exploration, and promotion of better physical and mental health. However, bike-sharing services are considerably less likely to be viewed as options for faster transportation to a destination or reducing traffic hazards. Notably, eighty-six percent of non-riders indicated contentment with their existing transportation options and a lack of interest in bike-sharing services, a proportion significantly higher than any other group. Predictably, barriers related to the availability of bike-sharing and infrastructure, such as lack of sufficient number of shared bikes, far destination, and poor road conditions were notably more likely to be selected by one-time riders. The results are also evident that a significant portion of tourists is willing to pay to enhance their tourist experience with a bike-sharing service. On average, tourists were willing to pay $0.92 per hour (with a standard deviation of $0.24). This amount reflects the tourists’ recognition of the value added to their mode experience. These findings suggest that bike-sharing service play a significant role in fulfilling an essential transportation niche and have the potential to contribute to enhance tourists’ experience. Efforts aimed at addressing barriers associated with bike-sharing usage could further enhance their contribution to improve tourist satisfaction and boost attraction demand.

Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis

Background:As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution. Method:To assess the short-term impact of daily exposure to multiple air pollutants (PM2.5, PM10, NO2, and O3) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018–2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM). The model was adjusted for day of the week, holiday, temperature, relative humidity, and long-term trend. We also conducted stratification analyses to examine the potential effect modification by age group, seasonality, and COVID-19. Results:We found that there was a negative association between daily ambient air pollution and the PBS usage level at a single lag day 1 (i.e., air quality a day before the event) across all four pollutants. Our results suggest that days with high levels of air pollutants (at 95th percentile) are associated with a 0.91% (0.86% to 0.96%) for PM2.5, 0.89% (0.85% to 0.94%) for PM10, 0.87% (0.82% to 0.91%) for O3, and 0.92% (0.87% to 0.98%) for NO2, reduction in cycling behavior in the next day compared to days with low levels of pollutants (at 25th percentile). No evidence of effect modification was found by seasonality, age nor the COVID-19 pandemic for any of the four pollutants. Conclusions:Our findings suggest that high concentrations of ambient air pollution are associated with decreased rates of PBS usage on the subsequent day regardless of the type of air pollutant measured.

Understanding bike-sharing usage patterns of members and casual users: A case study in New York City

Shared bicycle travel has become an important travel mode for urban residents, and bike-sharing platforms are also booming in major cities worldwide. The bike-sharing platform provides users with systematic services: members refer to annual bike-sharing service subscribers and casual users refer to holders of a day pass or single ride ticket. Even though casual users account for a large share of ridership and revenue at bike-share systems in New York City (NYC), very little is known about the characteristics and preferences of casual users and how they compare to members. Based on the open-docked bike-sharing dataset from Citi Bike, we analyze the bike usage patterns of members and casual users in NYC, and how these patterns change in the face of the COVID-19 pandemic on a typical day level. We find that the COVID-19 pandemic has negatively influenced members' bike trip counts on weekdays; bike travel time increases for members during the pandemic and decreases for casual users after the pandemic. To make a profound study concerning spatial heterogeneities, we employ Gaussian Mixture Model (GMM) to cluster the spatiotemporal changes of the station-level bike usage and obtain four clusters for each user type. Combined with the Points of Interest (POI) information, we find that member-related cluster with commuting POIs is significantly affected by the pandemic, while leisure trips are the most severely affected for casual users. Compared with the central area, peripheral clusters with residential and religious POIs are less affected by the pandemic. According to our findings, new operational strategies such as flexible subscriptions can be developed to attract more users, maintain their stickiness, and improve the bike-sharing level of services.

The heterogeneous effects of dockless bike-sharing usage intensity on house prices near subway stations

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

Exploring the factors affecting bike sharing usage in developing countries: a case study on Chandigarh, India

ABSTRACT Bike-sharing is one of the world’s fastest-growing new modes of transportation, with new schemes appearing on a regular basis. This paper examines the usage trends in a bike-sharing scheme operating in Chandigarh since 2020. While many studies have been conducted on how bike-sharing schemes are changing mobility in cities worldwide, particularly in developed countries, few have examined the dynamics of these schemes in cities in developing countries like India. One of the reasons for looking at a city like Chandigarh is to see if medium and smaller cities in developing countries benefit from bikesharing schemes and if bike-sharing schemes can play a prominent role in these cities. The study conducted an exploratory analysis using app data to examine the impact of factors such as temporal, weather, travel characteristics, and bike type (conventional or E-bike) on bike share usage in the city. Some of the research findings on the impact of various factors on bicycle ride durations (short, medium, and long) can assist other cities in developing countries in planning new bike station networks. Furthermore, analysing mode selection (conventional and E-bike) in bike share provides insights into user preferences and how various influencing factors influence their mode choice behaviour in similar cities.

A Systematic Review of the Coopetition Relationship between Bike-Sharing and Public Transit

The sharing economy, mobile Internet, and smartphones have been widely utilized in recent years to promote the development of bike-sharing services. Bike-sharing serves as a first/last mile travel mode to connect to public transit, which improves trip efficiency, alleviates traffic problems, improves environmental quality, and promotes public health. However, the substitution of public transit by bike-sharing and the decline in public transit ridership have raised concerns among city managers regarding the coopetition between shared mobility services and public transit. To understand the impact of bike-sharing on the decline in public transit and to formulate reasonable synergistic development policies, it is crucial to identify the coopetition relationships between the two. This paper uses a combination of database search and backward snowballing to review existing research. Three research themes were identified: macrolevel studies on bike-sharing and public transit interaction, studies on actual coopetition behaviors based on bike-sharing user surveys, and studies on potential coopetition relationships based on bike-sharing transaction data. The three categories of studies reveal the effect of bike-sharing usage on public transit ridership, the emergency function of bike-sharing in the event of unexpected transit shutdowns, and the substitution and connection relationships between bike-sharing and public transit and the factors influencing them. Finally, this study suggests many directions for future research. This review helps clarify the understanding of the coopetition relationships between bike-sharing and public transit, provides theoretical support to promote the synergistic development of both, and points out ways to deepen the research on the coopetition relationship between the two.

Tracing the effects of COVID-19 on short and long bike-sharing trips using machine learning

COVID-19 drastically changed human mobility, including bike-sharing usage. Existing studies found positive impacts of COVID-19 on bike-sharing use. However, their analysis focused on the first year of the COVID-19 pandemic. This study traces the effects of COVID-19 by including the bike-sharing data of the second and third years of the pandemic to provide more empirical evidence. We pre-defined short and long bike-sharing trips. Data collection and the effects of COVID-19 on both trips were separately addressed using public bike-sharing data in Seoul, South Korea. We conducted a time series and hot spot analysis to trace temporal and spatial bike-sharing usage changes. Our study applied a machine learning tool with Random Forest regression modeling to examine COVID-19 effects on two types of bike-sharing trips. Its impact is measured by looking at feature importance and calculating the SHapley Additive exPlanations (SHAP) value. The amount of bike-sharing usage continued to grow during the pandemic, with long bike-sharing trips being more prominent. A significant increase in the number of short bike-sharing trips was observed in the second year. Both short and long trips showed growth in the third year, even with a high number of COVID-19 cases reported. There were no significant seasonal changes in the spatial concentration of both trips. COVID-19 and the vaccination response positively impacted bike-sharing use in Seoul, highlighting our resilience in adapting to changes in human mobility.

Exploring the impacts of built environment on bike-sharing trips on weekends: The case of Guangzhou

Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments’ and enterprises’ efforts to improve the cycling environment and ensure the efficient management of shared bikes.

A non-local grouping tensor train decomposition model for travel demand analysis concerning categorical independent variables

With the booming of transportation big data, plenty of spatial and temporal features become available for developing travel demand analysis models. However, existing machine learning based travel demand fitting models could face significant model complexity issues once there are multiple discrete/categorical independent variables. This paper embeds a non-local grouping (clustering) process into the tensor train decomposition (NLG-TTD) to characterize the higher-order and nonlinear relationship between categorical variables and travel demand. That is, the transportation dataset with multiple discrete/categorical independent variables and travel demand is treated as a multi-mode tensor, wherein levels of a discrete/categorical variable are regarded as indexes of a mode in the tensor, leading to a “regression on index”. The proposed NLG-TTD model divides the full tensor into non-local sub-tensors, clusters sub-tensors with similarity, conducts tensor train decomposition (TTD) within each cluster, and aggregates cluster-specific learning results. Compared with a conventional TTD that generally works on image completions, NLG-TTD can reveal non-Euclidean and non-linear patterns between categorical/discrete levels in independent variables. It is expected to discover similar travel demand patterns among categorical levels and provide high-level goodness-of-fit. For numerical studies, we use bike-sharing usage data and metro demand data in New York City (NYC), in which several widely-used machine learning models are trained for comparison. The results demonstrate the good performance of the proposed model. Moreover, we apply the NLG-TTD model to analyze and predict the impact of land use shifts on travel demand.

Does docked bike-sharing usage complement or overlap public transport? the case of Brussels, Belgium

ABSTRACT This paper complements the existing literature on Bike Share Schemes (BSS) by investigating their use from a spatiotemporal perspective to assess their relationship with public transport (PT). We address these questions through the case of Brussels and its long-standing docked BSS ‘Villo!’. Our study analyses comprehensive (consecutive 12 months) and disaggregated (station level) data on rentals and returns and finds that Villo! is used mostly in dense (although not all) districts also well served by PT. However, temporal structures suggest Villo! overtakes PT at night in vibrant districts and possibly in selected districts with lower PT services over weekends. In addition, Villo! stations at key PT hubs usually do not show specific temporal patterns, which suggests intermodality may work at all times during PT operations. There could be an evening peak effect combined with the Brussels’ topography, but this needs to be confirmed by on-site surveys.

Examining the causal effects of air pollution on dockless bike-sharing usage using instrumental variables

Examining the causal effects of air pollution on dockless bike-sharing usage using instrumental variables